A0487
Title: Bayesian forecasting of tail risk in cryptocurrency markets
Authors: Cathy W-S Chen - Feng Chia University (Taiwan) [presenting]
Ying-Lin Hsu - National Chung Hsing University (Taiwan)
Po-Hui Chen - National Chung Hsing Universit (Taiwan)
Abstract: Cryptocurrencies exhibit high volatility, emphasizing the importance of accurately measuring tail risk in their markets. A threshold-switching mechanism is incorporated into Taylor's ES-CAViaR models that unveil features such as asymmetry and jump phenomena. These enhancements effectively capture the diverse tail risks of cryptocurrencies while enabling the simultaneous forecasting of both value-at-risk (VaR) and expected shortfall (ES). The proposed models incorporate two types of functions to address the VaR and ES nexus, with the option to use the rolling standard deviation of returns as a short-term volatility proxy as a regressor. The parameters and forecast tail risk are estimated within a Bayesian framework. Taking the two largest cryptocurrencies by market capitalization, Bitcoin and Ethereum, the one-step-ahead forecasting performance is assessed over a four-year out-of-sample period using a rolling window approach. The comparative results from backtests and five scoring functions among eight competing models support the conclusion that models with a threshold mechanism capture the tail risk of cryptocurrencies more accurately than other risk models.
